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zenodo56/100

Dataset for algorithmic thinking skills assessment: Results from the virtual CAT large-scale study in Swiss compulsory education

Overview This dataset was collected during a main study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education. As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention. Study Context, Location and Participants To comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's. The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons. Data Collection During the data collection process, session and participant details were manually recorded by the administrator. Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded.  Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. The assessment tool automatically tracked all user interaction within the platform. All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021).  Data collection was integrated into a validation module of the app.  Data Features The dataset comprises the following files: <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.csv</li> </ul> These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more. Usage & Ethics In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment.  Initial authorisations were secured from school administrators, teachers, and parents.  Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders. REFERENCES [1] A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a> [2] Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a> [3] Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a> [4] Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a>

opencc-by-4.0Apr 2024View details →

zenodo52/100

Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls

For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>. Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.   DESCRIPTION The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels. The dataset is organized into 5 pickle files: 1. imu_sdata.pickle: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples. Sample Python code for accessing the acceleration data of a subject <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> 2. imu_gdata.pickle: Same layout as imu_sdata.pickle but with data ffrom GData subjects. 3. typing_sdata.pickle: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502. Sample Python code for accessing the typing data of a subject: <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> 4. typing_gdata.pickle: Same layout as typing_sdata.pickle but with data from GData subjects. 5. subject_metadata.pickle: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:  'age': Year of birth,  'gender_id': 0 indicates male, 1 indicates female  'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.   ETHICS & FUNDING The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).   CORRESPONDANCE Any inquiries regarding this dataset should be adressed to: Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate) Multimedia Understanding Groupmug Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: alpapado@mug.ee.auth.gr

opencc-by-4.0Dec 2020View details →

zenodo52/100

WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine

<h1>1. General description </h1> This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine.  <h2>1.1 Summary of the shared structural information</h2> The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a> <h2>1.2 Summary of the shared geotechnical information</h2> Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016). The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., Undrained shear strength). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is: <ul> <li>"epsilon50 [-]" represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA).  <h2>1.3 Summary of the shared measurement data</h2> Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks.  The sensor network on NRT-WTG is illustrated in in Fig. 2, whereas a description of the sensor types is presented in Tab.1. The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall. <table> <tbody> <tr> <td>Data type </td> <td>Sensor type</td> <td>Fs (Hz)</td> <td> Level mLAT (m) </td> <td>Description </td> </tr> <tr> <td>Acceleration (g)  </td> <td>Piezo-electric acc. sensor (ACC)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (SG)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (FBG)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> Table 1. Description of sensor types. The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data.  The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset.   In conjunction to the sensor channels presented in Tab. 1, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in Tab 2. <table> <tbody> <tr> <td>Parameter</td> <td>Unit</td> <td>Description</td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> Table 2. List of provided SCADA parameters   A summary of the selected intervals and relevant corresponding scada parameters is given in Tab 3. <table> <tbody> <tr> <td>Scenario </td> <td>T1 (UTC)</td> <td>T2 (UTC) </td> <td>Windspeed</td> <td>RPM </td> <td>Pitch </td> </tr> <tr> <td>Parked</td> <td> 03/07  01:30 </td> <td> 03/07 03:30 </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> 05/07 22:30 </td> <td> 06/07 00:30  </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> Table 3. Selected data intervals and relevant scada parameters   <h1>2. Included in this version </h1> <h2>2.1 Version - 0.1.0</h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul>   <h1>3. Importing parquet files   </h1> To import the measurement data into Python it is recommended to use pandas: <pre>import pandas as pd # Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>

opencc-by-4.0Apr 2024View details →

zenodo52/100

Datasets for practical model selection for prospective virtual screening

This repository contains datasets for the manuscript "Practical model selection for prospective virtual screening": <ul> <li>pria_rmi_cv.tar.gz: A compressed directory containing chemical screening data for the PriA-SSB AS, PriA-SSB FP, and RMI-FANCM FP binary datasets.  The files also contain the associated continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.  The dataset has been split into five folds for cross validation.</li> <li>pria_rmi_pcba_cv.tar.gz: A compressed directory containing chemical screening data for the PriA-SSB AS, PriA-SSB FP, and RMI-FANCM FP binary datasets as well as public PubChem BioAssay datasets.  The files also contain the PriA-SSB and RMI-FANCM continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.  The dataset has been split into five folds for cross validation.  Missing values are left blank.</li> <li>pria_prospective.csv.gz: A compressed file containing chemical screening data for the binary dataset PriA-SSB prospective.  The file also contains the continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.</li> </ul> If you use these data in a publication, please cite: Shengchao Liu+, Moayad Alnammi+, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. Practical Model Selection for Prospective Virtual Screening. Journal of Chemical Information and Modeling. 2018 <a href="https://doi.org/10.1021/acs.jcim.8b00363">doi:10.1021/acs.jcim.8b00363</a> PubChem data were provided by the <a href="https://pubchem.ncbi.nlm.nih.gov/">PubChem database</a>.  Follow the <a href="https://pubchemdocs.ncbi.nlm.nih.gov/citation-guidelines">PubChem citation guidelines</a> if you use the PubChem data.  See <a href="https://doi.org/10.1177/2472555217712001">Voter et al. 2017</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>) for the PriA-SSB screening data and <a href="https://doi.org/10.1177/1087057116635503">Voter et al. 2016</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1159607">1159607</a>) for RMI-FANCM. Version 1.1.0 updates all of the data files.  We standardized the SMILES in all files by generating canonical SMILES with RDKit version 2016.03.4.  In addition, we removed 2845 chemicals from pria_prospective.csv.gz that were duplicates of compounds in pria_rmi_cv.tar.gz.

opencc-by-4.0May 2018View details →

zenodo52/100

Dataset for algorithmic thinking skills assessment: Results from the virtual CAT pilot study in Swiss compulsory education

Overview This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education. As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.Study Context, Location and Participants To demonstrate the virtual CAT's effectiveness, we conducted a pilot study in March 2023. The study was conducted in Switzerland, specifically within the Ticino canton. The sample consisted of 31 students (21 girls and 10 boys) from a preschool class (ages 4-6) and two low secondary classes (1st grade, ages 11-12). Data Collection Data collection was integrated into a validation module of the app.  Sessions required manual input for details like date, canton, and school information.  Students' details, anonymised for privacy, encompassed their gender and date of birth.  Each interaction within the platform was meticulously logged, capturing operations like task confirmations, command updates, mode changes, and more.Data Features The dataset comprises the following files:<ul><li>ALGORITHMS.csv</li><li>CANTONS.csv</li><li>DF.csv</li><li>LOGS.csv</li><li>RESULTS.csv</li><li>SCHOOLS.csv</li><li>SESSIONS.csv</li><li>STUDENTS_SESSIONS.csv</li></ul>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.Usage & Ethics In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment.  Initial authorisations were secured from school administrators, teachers, and parents.  Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders. REFERENCES[1] A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a>[2] Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a>[3] Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a>[4] Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a>

opencc-by-4.0Oct 2023View details →

zenodo48/100

Virtually Real short film "Sonnenwende" by Timo von Gunten, versions with real and virtual backgrounds, 8min35sec, german, ProRes 1998 × 1080

Two short feature films were recorded with a virtual background by using a green screen in a film studio and previously scanned 3D spaces in a virtual production environment and conventionally (in the corresponding real spaces) to compare the aesthetics and perception of these films. One scene of each film is shown in two variants (real vs. virtual background) to make a comparison. These 2 QuickTime files represents both the "real" and "virtual" version of the short Film "Sonnenwende" (2019) by Timo von Gunten. The real version was shot on location using real backgrounds, the virtual version was shot in the studio in front of a green-screen using virtual backgrounds that were added in post production.  Running time: 8min35sec. This is the highest quality version available.  Virtually Real is a research project by the Zurich University of the Arts, Institute for the Performing Arts and Film and the University of Bern, Institute of Psychology.

opencc-by-4.0Nov 2020View details →

zenodo48/100

Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1

Small Angle Neutron Scattering (SANS) virtual experiments at KWS-1, FRM-II dataset. Intended for Machine learning purposes. Data generated by performing simulations in <a href="https://www.mcstas.org/">McStas</a> with the <a href="https://www.sasview.org/docs/user/qtgui/Perspectives/Fitting/models/index.html">SasView small angle scattering form factor models</a> describing the sample interaction. Two parameter spaces are varied sistematically: form factor model parameters and instrument configuration parameters. For more detailed information, read the <code>README.md</code> file of this database. The database contains 46 SANS form factor models under different instrument configurations. All data is uploaded in <code>hdf5</code> files, and the corresponding metadata in <code>.csv</code> files. Description of what each instrument configuration means (sample-detector distance, collimation, incident wavelength) and which model is used is contained in the metadata file.  Each array is the result of the position sensitive detector output in neutron intensity (float values). A Dataset loader for Pytorch may be found <a href="https://github.com/jorobledo/hdf_loader_pytorch" target="_blank" rel="noopener">in GitHub</a> and is intended for Machine Learning purposes.

opencc-by-4.0Mar 2024View details →

zenodo48/100

Virtual Research Environments Ethnography: a Preliminary Study

Datasets accompanying the paper “Virtual Research Environments Ethnography: a Preliminary Study”, a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories. While for legal reasons we can not share the original datasets obtained by querying the databases, since they include copyrighted data, we can share the two datasets derived from the query results and the two topic modelling datasets. The dataset “main_dataset.csv” consists of the merged query results from ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink databases. It is structured into six columns: (i) doi; (ii) title; (iii) content_type; (iv) publication year; (v) keyword_search; (vi) DB. The ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘content_type’ label refers to the different and normalised typologies of resources: (a) Article; (b) Book, (c) Book Chapter; (d) Chapter; (e) Chapter ReferenceWorkEntry; (f) Conference Paper; (g) Conference Review; (h) Early Access Articles; (i) Editorial; (j) Erratum; (k) Letter; (l) Magazines; (m) Masters Thesis; (n) Note; (o) Ph.D. Thesis; (p) Retracted; (q) Review; (r) Short Survey; (s) Standards. (c) and (d) refer to the same type of entry (they are used in different databases), while in the case of (e) we observed that it is used in the Springer database to refer mainly to encyclopaedic entries. The ‘keyword_search’ label is used for identifying the keyword group used for formulating the query: (a) science gateway | scientific gateway; (b) virtual laboratory | Vlab; or (c) virtual research environment. The ‘DB’ label indicates the provenance of the entries from one of the five databases we selected for our study: (a) ACM; (b) IEEE; (c) ScienceDirect; (d) scopus; and (e) Springer, identifying the ACM Digital Library, IEEEXplore, ScienceDirect, Scopus, and SpringerLink respectively. The dataset “filtered_dataset.csv” consists of the deduplicated and filtered entries (journal articles and conference papers from 2010 onward, with a DOI assigned) from the “main_dataset.csv” we used as the final dataset for answering our research questions. It is structured into ten columns: (i) doi; (ii) title; (iii) venue; (iv) publication_year; (v) content_type; (vi) abstract; (vii) keywords; (viii) science gateway | scientific gateway; (ix) virtual laboratory | Vlab; and (x) virtual research environment. As for the previous dataset, the ‘doi’, ‘title’, and ‘publication_year’ labels are self-describing, and are used for the DOIs, titles, and publication years (in the yyyy format) respectively. The ‘venue’ label is used for indicating the conference or the journal the entries refer to. The values derive from the original query results. The ‘abstract’ and ‘keyword’ labels are used for the abstracts and the keywords associated with the entries. The values are mainly derived from the original query results, as we integrated the missing ones by querying OpenAIRE. The ‘science gateway | scientific gateway’, ‘virtual laboratory | Vlab’ and ‘virtual research environment’ labels indicate the connection between the entries and the keyword group used for denoting them. The values are binary (1 if the keywords belong to the group, 0 if they do not). The datasets “sg_vlab_vre_topics_datasets.csv” and “sgvlabvre_topics_dataset.csv” consist of the three datasets and of the unique dataset resulting from topic modelling, the first (corpus divided into three datasets) and the second analysis (corpus as a whole) respectively. They share the same structure: (i) Topic; (ii) #studies; (iii) Representative word; (iv) Representative word weight. The ‘Topic’ label is used for the topic denomination and the values consist of an alphanumeric string indicating the dataset and the progressive topic number: (a) SG, for the scientific gateway dataset; (b) VRE, for the virtual research environment dataset; (c) VLAB, for the virtual laboratory dataset; and (d) A, for the corpus as a whole. The ‘#studies’ label indicates the number of studies contributing to each topic. The ‘Representative word’ and ‘Representative word weight’ labels are used for denoting the keywords describing each topic and their weights respectively.

opencc-by-4.0Dec 2021View details →

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Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality Dataset

This dataset accompanies the paper “Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality” published in the Computer and Graphics journal from Elsevier. It contains 3 datasets related to the experiments described in the paper. All datasets are in “.csv” format and can be easily loaded by statistical analysis tools (e.g. a dataset can be loaded in r using the command read.csv(“filename.csv”)). It also contains the C# Unity implementation of the distortion function presented in the paper. Paper reference: Galvan Debarba H, Boulic R, Salomon R, Blanke O, Herbelin B. Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality. Computers & Graphics. 2018; ISSN 0097-8493. Elsevier. DOI: doi.org/10.1016/j.cag.2018.09.001

opencc-by-4.0Sep 2018View details →

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Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment

Dataset name: asppl-dataset.csv Version: 1.0 Dataset period: 06/07/2018- 05/25/2021 Dataset Characteristics: Multivalued Number of Instances: 4861 Number of Attributes: 33 Missing Values: Yes Area(s): Health and education  Sources:  <ul> <li> Primary: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1]; </li> <li> Secondary:  <ol> <li> Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2]; </li> <li> National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and  </li> <li> Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4]. </li> </ol> </li> </ul> Description: The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.

opencc-by-4.0Jul 2021View details →

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Virtual Reality Dataset used for Proof of Concept in the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION)

This dataset contains the dataset used in the Virtual Reality POC for the validation of the Conflict Detection and Resolution (CD&R) use case. This dataset represent a extract of different (using K-means) candidate solution, either good or bad ones.

opencc-by-4.0Dec 2021View details →

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Egnazia (Fasano, BR). La domus ad atrio romana nell'isolato a sud del foro, ricostruzione virtuale e backend scientifico

La ricostruzione virtuale (Altair4 Multimedia in collaborazione con il team dell'Università di Bari guidato da Gianluca Mastrocinque), ha interessato questa dimora ancora in corso di scavo, per la quale erano già disponibili dati consistenti, che sono stati sistematizzati per la prima pubblicazione nello stesso periodo di lavorazione del 3D. Oltre alla vista dall’esterno per il rapporto con il tessuto urbano (V01), sono stati ricostruiti dall’interno  4 spazi: l’atrio da cui si ha la percezione più efficace dello sviluppo degli spazi (V02), il tablino (V03), la dispensa (V06), il sacrario (V04), vano documentato di rado, specie nella Puglia di età romana. Per ogni ambiente i dati dello scavo stratigrafico hanno permesso di ricostruire e ricollocare molti elementi della decorazione e dell’arredo, mentre altri sono stati elaborati sulla base di confronti stringenti per tipologia e cronologia, come risulta dal back-end scientifico. Ad esempio, nel sacrario, è stato ricollocato l’altare sulla base di un grande frammento reimpiegato in età tardoantica e si è realizzata l’anastilosi virtuale della statua di Demetra custodita presso il Museo, mentre nella dispensa lo studio già completo dei materiali ha consentito di ricostruire con alto grado di dettaglio suppellettili, merci e vivande. Il modello tridimensionale è stato realizzato con Autodesk 3DSTUDIO MAX, e renderizzato con V-RAY. Sono incluse nel dataset le immagini panoramiche a 360° dello stato attuale dei 5 punti, realizzate con camera INSTA360 PRO2 per le riprese a terra, e i rendering a 360° realizzati dai medesimi punti di vista. A ciascuno dei 5 punti di vista panoramici sono associate le immagini di backend, dove il livello di affidabilità della ricostruzione è indicato con colori simbolici e supportato da informazioni su fonti e processi interpretativi, secondo il metodo dell’extended matrix.

opencc-by-4.0Dec 2022View details →

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Egnazia, Fasano (BR). Impianto produttivo di età tardoantica nell'area delle terme e di una domus romana, ricostruzione virtuale delle fornaci e backend scientifico

La ricostruzione virtuale delle fornaci per la calce nell’area produttiva tardoantica che riconverte lo spazio delle terme del foro e di una domus romana attigua è stata realizzata da Altair4 Multimedia sulla base dei dati di scavo e di rilievo (piante di fase) forniti dal team della Università di Bari guidato da Gianluca Mastrocinque. Le due fornaci erano situate in due ambienti della precedente dimora. L’impianto di maggiore portata, a carica dal basso, era definito da una struttura circolare in grandi blocchi ed era provvisto di un piano di cottura in lastre di calcare, tra cui alcuni basoli della via Traiana ormai ricoperta da una massicciata.  Sono stati individuati 2 punti di vista utili a visualizzare la ricostruzione delle due fornaci sovrapposta allo stato attuale dell’area. Il modello tridimensionale è stato realizzato con BLENDER 3.0, e renderizzato con V-RAY. Sono incluse nel dataset le immagini panoramiche a 360° dello stato attuale dei 2 punti prescelti realizzate con camera INSTA360 PRO2, e i rendering a 360° realizzati dai medesimi punti di vista. A ciascuno dei 2 punti di vista panoramici sono associati i contenuti narrativi in italiano e inglese per lo stato attuale e per la ricostruzione, oltre alle immagini di backend con le informazioni relative alle fonti e ai processi interpretativi che hanno supportato l'ipotesi ricostruttiva.

opencc-by-4.0Dec 2022View details →

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DATA: Linking Personality and Trust in Intelligent Virtual Assistants

This dataset (n=367) investigates links between people's personality, their trust in intelligent virtual agents (e.g., Amazon's Alexa, Apple's Siri, etc.) and their affinity for technology interaction.

opencc-by-4.0Mar 2023View details →

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PrimeWater Virtual Lab: Experiment C1

Input and output data for Experiment C1 of PrimeWater's Virtual Lab. Experiment Name: Assessing the relevance of hydrometeorological variables in data-driven forecasting of phytoplankton dynamics in surface waters

opencc-by-4.0Mar 2023View details →

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PrimeWater Virtual Lab: Experiment C3

Input and output data for Experiment C3 of PrimeWater's Virtual Lab. Experiment Name: Quantile-based modeling of phytoplankton dynamics using Random Forests

opencc-by-4.0Apr 2023View details →

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PrimeWater Virtual Lab: Experiment C2

Input and output data for Experiment C2 of PrimeWater's Virtual Lab. Experiment Name: Develop and benchmark data-driven algorithms to forecast the short-term dynamics of phytoplankton in surface water reservoirs

opencc-by-4.0May 2023View details →

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PrimeWater Virtual Lab: Experiment B4

Input and output data for Experiment B4 of PrimeWater's Virtual Lab. Experiment Name: Developing an error-correcting complementary modeling approach for surface water quality

opencc-by-4.0May 2023View details →

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Dataset for AvA (Accelerated Virtualization of Accelerators) in ASPLOS'20

This is the dataset used by the paper titled "AvA: Accelerated Virtualization of Accelerators" which will appear in ASPLOS'20.

openapache2.0Dec 2019View details →

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Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll

Data and code to perform the"Target deformation" workflow in R: virtual reconstruction of the Equus stenonis holotype skulll. TargetDeformation.R: R code with for the Target Deformation procedure. IGF560.ply: 3D mesh of the holotype IGF560 in ply extension. IGF560_set.txt: landmark set of the holotype IGF560. Dm. 5/154.3/4.A4.5.ply: 3D mesh of Dm 5/154.3/4.A4.5 in .ply extension. Dm_set.txt: landmark set of the Dm 5/154.3/4.A4.5 sample. IGF11023: 3D mesh of IGF11023 in.ply extension. IGF11023_set.txt: landmark set on the IGF11023 sample. IGF560R: 3D mesh of IGF560R in.ply extension. IGF560W: 3D mesh of IGF560W in.ply extension. IGF560R-s: 3D mesh of IGF560R-s in.ply extension. IGF560W-s: 3D mesh of IGF560W-s in.ply extension. IGF560_IGF560R_IGF560W.html: file that contain WebGL code to reproduce the 3D meshes of IGF560, IGF560R and IGF560W in a browser. IGF560Rs_IGF560Ws.html: file that contain WebGL code to reproduce the 3D meshes of IGF560R-S and IGF560W-S in a browser. IGF560W Mesh area variation.html: file that contain WebGL code to reproduce two 3d meshes of IGF560W using localmeshDist() and meshdist() in a browser.

opencc-by-4.0May 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record